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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-05132022-134024


Tipo di tesi
Tesi di laurea magistrale
Autore
MANUELE, ANTONIO
URN
etd-05132022-134024
Titolo
Differential Flatness Methods for Trajectory Optimization of Racing Vehicles
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
INGEGNERIA ROBOTICA E DELL'AUTOMAZIONE
Relatori
relatore Prof. Gabiccini, Marco
correlatore Ing. Bartali, Lorenzo
correlatore Ing. Mugnai, Michael
Parole chiave
  • autonomous racing
  • differential flatness
  • model predictive control
  • motion planning
  • optimization
  • spatial formulation
  • vehicles
Data inizio appello
01/06/2022
Consultabilità
Non consultabile
Data di rilascio
01/06/2092
Riassunto
One of the most relevant problems in vehicle dynamics control is the computational
burden due to optimal trajectory generation. Exploiting the differential flatness property and implementing a different approach to Nonlinear Programming (NLP) it is demonstrated that the computational time to complete the optimization decreases considerably compared to classical direct methods. Two vehicle models, Point-Mass and Torque Vectoring Single-Track, are proven to be flat and employed for offline optimization. Point-Mass is also used as nominal model in the development of an extremely efficient Flatness-Based Model Predictive Control (FMPC) for the Formula SAE race car of the University of Pisa which is validated through simulations. Trajectory generation is formulated in spatial domain, that allows to describe the pose of the vehicle in track reference frame.
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